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Dmitry Chechik

from San Carlos, CA
Age ~41
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Related to:
Leor Chechik, 41
Connected to:
Philip Chechik, 65Anthony Chechilo, 76Hope M Chechile, 51Louis A Chechile, 77Phyllis C Chechile, 71Andrea K Checho, 50Noreen Chechopoulos, 62Adam P Checinski, 59Richard S Checinski, 74Abigail L Check, 39

Dmitry Chechik Phones & Addresses

  • 230 Hillcrest Rd, San Carlos, CA 94070
  • Waverley St, Palo Alto, CA 94301
  • Mountain View, CA
  • San Francisco, CA

Work

Company: Pinterest Feb 2012 Position: Software engineer

Education

Degree: BSE School / High School: University of Waterloo 2002 to 2007 Specialities: Software Engineering

Skills

Python • Distributed Systems • Java • Hadoop • Scalability • Git • Software Engineering • Algorithms • Mapreduce • C++ • Software Development • Linux • Software Design • Big Data • Hbase

Industries

Computer Software

Public records

Vehicle Records

Dmitry Chechik

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Address:
539 Hillcrest Rd, San Carlos, CA 94070
Phone:
(650) 265-7889
VIN:
KNAGM4A71B5118583
Make:
KIA
Model:
OPTIMA
Year:
2011

Resumes

Resumes

Dmitry Chechik Photo 1

Director, Detection Engineering

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Location:
230 Hillcrest Rd, San Carlos, CA 94070
Industry:
Computer Software
Work:
Pinterest since Feb 2012
Software Engineer
TellApart Nov 2009 - Feb 2012
Senior Software Engineer
Google Aug 2007 - Oct 2009
Software Engineer
Google Jan 2006 - Dec 2006
Software Engineer Intern
Research In Motion Sep 2004 - Aug 2005
Software Designer (coop)
Education:
University of Waterloo 2002 - 2007
BSE, Software Engineering
Skills:
Python
Distributed Systems
Java
Hadoop
Scalability
Git
Software Engineering
Algorithms
Mapreduce
C++
Software Development
Linux
Software Design
Big Data
Hbase

Publications

Us Patents

Multistage Analysis Of Emails To Identify Security Threats

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US Patent:
20220278997, Sep 1, 2022
Filed:
Feb 22, 2022
Appl. No.:
17/677822
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - San Francisco CA, US
Dmitry Chechik - San Francisco CA, US
Abhijit Bagri - San Francisco CA, US
Evan Reiser - San Francisco CA, US
Sanny Xiao Lang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - San Francisco CA, US
Kevin Lau - San Francisco CA, US
Kai Jiang - San Francisco CA, US
Su Li Debbie Tan - San Francisco CA, US
Jeremy Kao - San Francisco CA, US
Cheng-Lin Yeh - San Francisco CA, US
International Classification:
H04L 9/40
G06F 16/955
G06F 16/958
G06N 20/00
G06Q 10/10
G06F 16/951
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Retrospective Learning Of Communication Patterns By Machine Learning Models For Discovering Abnormal Behavior

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US Patent:
20210329035, Oct 21, 2021
Filed:
Jun 28, 2021
Appl. No.:
17/361106
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US
International Classification:
H04L 29/06
G06N 20/00
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Programmatic Discovery, Retrieval, And Analysis Of Communications To Identify Abnormal Communication Activity

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US Patent:
20210297444, Sep 23, 2021
Filed:
Jun 7, 2021
Appl. No.:
17/341200
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US
International Classification:
H04L 29/06
G06Q 10/10
G06F 16/901
H04L 12/24
H04L 12/58
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Retrospective Learning Of Communication Patterns By Machine Learning Models For Discovering Abnormal Behavior

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US Patent:
20200396258, Dec 17, 2020
Filed:
Jul 13, 2020
Appl. No.:
16/927335
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US
International Classification:
H04L 29/06
G06N 20/00
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Programmatic Discovery, Retrieval, And Analysis Of Communications To Identify Abnormal Communication Activity

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US Patent:
20200389486, Dec 10, 2020
Filed:
Jul 13, 2020
Appl. No.:
16/927478
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US
International Classification:
H04L 29/06
G06Q 10/10
H04L 12/58
H04L 12/24
G06F 16/901
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Multistage Analysis Of Emails To Identify Security Threats

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US Patent:
20200344251, Oct 29, 2020
Filed:
Jul 13, 2020
Appl. No.:
16/927427
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US
International Classification:
H04L 29/06
G06F 16/958
G06F 16/955
G06F 16/951
G06Q 10/10
G06N 20/00
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Threat Detection Platforms For Detecting, Characterizing, And Remediating Email-Based Threats In Real Time

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US Patent:
20200204572, Jun 25, 2020
Filed:
Nov 4, 2019
Appl. No.:
16/672854
Inventors:
- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US
International Classification:
H04L 29/06
Abstract:
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.
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Dmitry Chechik from San Carlos, CA, age ~41 Get Report